{"id":"https://openalex.org/W2934241447","doi":"https://doi.org/10.1109/hri.2019.8673170","title":"Emulating Touch Signals from Multivariate Sensor Data Using Gated RNNs","display_name":"Emulating Touch Signals from Multivariate Sensor Data Using Gated RNNs","publication_year":2019,"publication_date":"2019-03-01","ids":{"openalex":"https://openalex.org/W2934241447","doi":"https://doi.org/10.1109/hri.2019.8673170","mag":"2934241447"},"language":"en","primary_location":{"id":"doi:10.1109/hri.2019.8673170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hri.2019.8673170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039793968","display_name":"Seung-Chan Kim","orcid":"https://orcid.org/0000-0001-7292-5166"},"institutions":[{"id":"https://openalex.org/I146824383","display_name":"Hallym University","ror":"https://ror.org/03sbhge02","country_code":"KR","type":"education","lineage":["https://openalex.org/I146824383"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seung-Chan Kim","raw_affiliation_strings":["Intelligent Robotics Lab., Hallym University, Chuncheon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Robotics Lab., Hallym University, Chuncheon, Korea","institution_ids":["https://openalex.org/I146824383"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059725173","display_name":"Byung-Kil Han","orcid":"https://orcid.org/0000-0003-4796-8708"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byung-Kil Han","raw_affiliation_strings":["Dept. of Mechanical Engineering, KAIST, Daejeon, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Mechanical Engineering, KAIST, Daejeon, Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0014,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.75598028,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"15","issue":null,"first_page":"628","last_page":"629"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11349","display_name":"Music Technology and Sound Studies","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6784511804580688},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6099798083305359},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5454897880554199},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4495941698551178},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14045143127441406}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6784511804580688},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6099798083305359},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5454897880554199},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4495941698551178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14045143127441406}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hri.2019.8673170","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hri.2019.8673170","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W161505811","https://openalex.org/W2095705004","https://openalex.org/W2130649401","https://openalex.org/W2139648013","https://openalex.org/W2157331557","https://openalex.org/W2161649348","https://openalex.org/W2902233445","https://openalex.org/W6674330103"],"related_works":["https://openalex.org/W1891287906","https://openalex.org/W1969923398","https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2755342338","https://openalex.org/W2229312674","https://openalex.org/W3116076068","https://openalex.org/W2058170566","https://openalex.org/W258625772","https://openalex.org/W2170022336"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,21,51,64,67,83],"sensor":[4,10,23,33,120],"substitution":[5],"system":[6,29,47,98],"that":[7],"generates":[8],"time-series":[9],"data":[11],"via":[12],"recurrent":[13,85],"neural":[14],"networks":[15],"(RNN)-based":[16],"sequence":[17,79],"analysis":[18],"to":[19,103],"regress":[20],"virtual":[22],"sequence.":[24],"More":[25],"specifically,":[26],"the":[27,77,93,96,117],"proposed":[28,46,97],"estimates":[30],"capacitive":[31,68],"touch":[32,69,119],"signals":[34,41,105,112],"by":[35,43],"exploiting":[36],"tiny":[37],"motion":[38],"and":[39,66,99,108],"audio":[40,111],"generated":[42],"touch.":[44],"The":[45,72,88],"was":[48,80],"validated":[49],"in":[50,55],"supervised":[52],"learning":[53],"task":[54],"which":[56],"multiple":[57],"sensors-specifically,":[58],"an":[59,62],"omnidirectional":[60],"microphone,":[61],"accelerometer,":[63],"gyroscope,":[65],"sensor-were":[70],"employed.":[71],"multivariate":[73],"temporal":[74],"information":[75],"of":[76,95],"input":[78],"modelled":[81],"using":[82],"gated":[84],"unit":[86],"(GRU).":[87],"experimental":[89],"results":[90],"obtained":[91],"verified":[92],"feasibility":[94],"indicated":[100],"that,":[101],"compared":[102],"inertial":[104],"(e.g.,":[106],"acceleration":[107],"angular":[109],"velocities),":[110],"are":[113],"better":[114],"for":[115],"estimating":[116],"corresponding":[118],"sequences.":[121]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
